Timed fingerprint locating in wireless networks

ABSTRACT

A location of user equipment (UE) in a wireless network is determined. Geometric calculations are leveraged for an overlaid bin grid framework mapping the wireless network area to store differential values for each frame of the bin grid framework for each pair of relevant NodeBs. A timing offset can be determined, such that when a time value from a target UE is accessed, the location can be quickly determined. The target UE time value can be searched for in the pre-computed differential value data set indexed by a relevant NodeB site pair to return a set of frames (forming a hyperbola between the site pair) that can be intersected with a second set of frames for a second NodeB site pair for the same UE. The intersecting frames can represent the location of the UE in the wireless network and timing in the network is correctable based on the data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application claims priority to, and is a continuation of,U.S. patent application Ser. No. 13/523,778, filed Jun. 14, 2012, andentitled “TIMED FINGERPRINT LOCATING IN WIRELESS NETWORKS,” which claimspriority to, and is a continuation of, U.S. patent application Ser. No.12/712,424, filed Feb. 25, 2010, now issued as U.S. Pat. No. 8,224,349,and entitled “TIMED FINGERPRINT LOCATING IN WIRELESS NETWORKS,” whichpatent applications are each incorporated herein by reference in theirrespective entireties.

TECHNICAL FIELD

The subject disclosure relates to wireless communications and, moreparticularly, to location determinations of mobile equipment in awireless network environment.

BACKGROUND

In mobile equipment networks, locating user equipments (UEs) can providevaluable additional benefits to users and opportunities for additionalor improved services. Typical mobile equipment networks provide wirelessaccess to various communications services for UEs, such as voice, video,data, messaging, content broadcast, VoIP, etc. Wireless networks typescan include Universal Mobile Telecommunications System (UMTS), Long TermEvolution (LTE), High Speed Packet Access (HSPA), Code Division MultipleAccess (CDMA), Time Division Multiple Access (TDMA), Frequency DivisionMultiple Access (FDMA), Multi-Carrier Code Division Multiple Access(MC-CDMA), Single-Carrier Code Division Multiple Access (SC-CDMA),Orthogonal frequency division multiple access (OFDMA), Single-carrierFDMA (SC-FDMA), etc.

Locating UEs in a wireless network can facilitate providinglocation-centric services or information in relation to the UE, such asE911 services, mapping services, or traffic information services, amongmany others. Additionally, UE location information can be employed toimprove network performance, to troubleshoot networks, by lawenforcement, to aggregate valuable demographic information, or nearly alimitless number of other uses. Such additional usage of UE locationdata can proactively include removal or obfuscation of identifyinginformation at various levels to address privacy concerns.

Traditional methods of determining UE locations include measuring thetiming delay of the signals transmitted between the wireless basestation and the wireless handset and applying various location servicesor methods, including, but not limited to, cell global identity andtiming advance (CGI+TA), CGI and round trip time (CGI+RTT), time ofarrival (TOA), or other custom methods. Network timing delays includesite timing delay in the wireless signal path among radio component(s)at the wireless base station and a sector antenna. Network timing delaysfurther include delays that can arise from various mismatches (e.g.,impedance mismatch) among electronic elements and components, straycapacitances and inductances, length of the antenna(s) cable(s) in basestation(s); tower height of base station, signal path scattering, or“signal bounces,” such as multipath or strong reflections, and the like.Propagation delay between a UE and a NodeB is conventionally assumed tobe negligible with respect to timing delay. However, depending on thearchitecture of the serving base station and covered sector antenna(s)signal propagation delay can be substantive, particularly in distributedantenna systems and low-power wireless radio cells and cause significanterror in UE location determinations for traditional methods.

It is becoming more common to try to determine propagation delay withimproved accuracy so as to improve UE location calculations.Conventional UE location techniques include, but are not limited to,Cell ID (CID) wherein errors of multiple km are expected, Enhanced CID(ECID) which includes mobile timing advance and allows location of themobile within an arc some distance from the base site and errors canstill be multiple km, RF signal strength (RSSI) reported by the UE oftenhaving errors of >1 km due to RSSI variation, Round Trip Time (RTT) formultilateration from three or more base sites with errors often >1 km,Uplink Time Difference of Arrival (UTDOA) using specialized receivers onthree or more base stations to measure the propagation time differencebetween the mobile and sites, or Assisted GPS (AGPS) using a GPSreceiver in a UE to compute its own location to <10 m where satellitecoverage is accessible (which can be <70% of the time.)

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates a schematic exemplary wireless environment that canoperate in accordance with aspects of the disclosed subject matter.

FIG. 2 is a diagram of a subset of wireless devices (UEs) in a timefingerprint location wireless environment, in accordance with aspects ofthe disclosed subject matter.

FIG. 3 is a diagram illustrating differential propagation delay sets forNBSPs in a time fingerprint location wireless environment in accordancewith aspects described herein.

FIG. 4 is a block diagram of an exemplary system that facilitatescalibration of wireless signal propagation delay in accordance withaspects described herein.

FIG. 5 is a diagram depicting additional or alternative timing and/orlocation data generation components facilitate calibration of referenceframes in accordance with aspects described herein.

FIG. 6 presents a flowchart of an exemplary method 600 for determining alocation for a UE.

FIG. 7 is a flowchart of an exemplary method 700 for iterativelydetermining a location of a UE in a TFL wireless network according toaspects of the disclosed subject matter

FIG. 8 presents a flowchart of an exemplary method 800 for updating sitetiming values according to aspects of the disclosed subject matterdescribed herein.

FIG. 9 illustrates a block diagram of an exemplary embodiment of anaccess point to implement and exploit one or more features or aspects ofthe disclosed subject matter.

FIG. 10 is a block diagram of an exemplary embodiment of a mobilenetwork platform to implement and exploit various features or aspects ofthe disclosed subject matter.

DETAILED DESCRIPTION

The disclosed subject matter is now described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the disclosed subject matter. It may beevident, however, that the disclosed subject matter may be practicedwithout these specific details. In other instances, well-knownstructures and devices are shown in block diagram form in order tofacilitate describing the disclosed subject matter.

One or more embodiments of the disclosed subject matter, in order toprovide a basic understanding of some aspects of the invention,facilitate determining the location(s) of UE(s) in a wireless network.This can facilitate determinations of propagation delay values ofwireless signals that can be leveraged to improve network performance,e.g., to allow for correction of wireless network system timing.Further, location determinations can facilitate location-centricservices and information related to the UE (e.g., mapping, points ofinterest, etc.) and/or by other entities (e.g., E911, traffic analysis,population demographics, etc.)

Wireless signals can be radio frequency signals, microwave signals, orother electromagnetic waves employed for telecommunication. Compensationof signal path propagation is accomplished for sources of delay, suchas, for example, mismatches (e.g., impedance mismatch) among electronicelements and components, stray capacitances and inductances, length ofthe antenna(s) cable(s) in base station(s); tower height of basestation, signal propagation scattering, or “signal bounces,” such asmultipath or strong reflections, etc. Signal path compensation iseffected, at least in part, through determination of a propagationdelay. Such determination is based, at least in part, on statisticalanalysis of the location of UEs (or other reference locations)throughout a coverage sector or cell. These UE locations can begenerated through time fingerprint locating (TFL) measurements ofwireless signals. In an aspect, high-accuracy (e.g., 1 m-10 m) locationestimates of select mobile devices, such as estimates obtained by way ofassisted global positioning system (AGPS) or other global navigationsatellite systems (GNSSs), e.g., Galileo or GLONNAS, can be leveraged inlocating UEs.

Aspects, features, or advantages of the various embodiments of thesubject disclosure can be exploited in wireless telecommunicationdevices, systems or networks. Non-limiting examples of such devices ornetworks include Femto-cell technology, Wi-Fi, WorldwideInteroperability for Microwave Access (WiMAX); Enhanced General PacketRadio Service (Enhanced GPRS); Third Generation Partnership Project(3GPP) Long Term Evolution (LTE); 3GPP UMTS; Third GenerationPartnership Project 2 (3GPP2) Ultra Mobile Broadband (UMB); High SpeedPacket Access (HSPA); High Speed Downlink Packet Access (HSDPA); HighSpeed Uplink Packet Access (HSUPA); GSM Enhanced Data Rate for GSMEvolution (EDGE) Radio Access Network (RAN) or GERAN; UMTS TerrestrialRadio Access Network (UTRAN); or LTE Advanced. Additionally, aspects ofthe disclosed subject matter can include legacy telecommunicationtechnologies. This summary is not an extensive overview of theinvention. It is intended to neither identify key or critical elementsof the invention nor delineate the scope of the invention. Its solepurpose is to present some concepts of the invention in a simplifiedform as a prelude to the more detailed description that is presentedlater.

To the accomplishment of the foregoing and related ends, the invention,then, comprises the features hereinafter fully described. The followingdescription and the annexed drawings set forth in detail certainillustrative aspects of the invention. However, these aspects areindicative of but a few of the various ways in which the principles ofthe invention may be employed. Other aspects, advantages and novelfeatures of the invention will become apparent from the followingdetailed description of the invention when considered in conjunctionwith the drawings.

As used in this application, the terms “component,” “system,”“platform,” “layer,” “selector,” “interface,” and the like are intendedto refer to a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration, both an application runningon a server and the server can be a component. One or more componentsmay reside within a process and/or thread of execution and a componentmay be localized on one computer and/or distributed between two or morecomputers. Also, these components can execute from various computerreadable media having various data structures stored thereon. Thecomponents may communicate via local and/or remote processes such as inaccordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can include a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form.

Moreover, terms like “user equipment (UE),” “mobile station,” “mobile,”subscriber station,” “subscriber equipment,” “access terminal,”“terminal,” “handset,” and similar terminology, refer to a wirelessdevice utilized by a subscriber or user of a wireless communicationservice to receive or convey data, control, voice, video, sound, gaming,or substantially any data-stream or signaling-stream. The foregoingterms are utilized interchangeably in the subject specification andrelated drawings. Likewise, the terms “access point (AP),” “basestation,” “Node B,” “evolved Node B (eNode B),” “home Node B (HNB),”“home access point (HAP),” and the like, are utilized interchangeably inthe subject application, and refer to a wireless network component orappliance that serves and receives data, control, voice, video, sound,gaming, or substantially any data-stream or signaling-stream from a setof subscriber stations. Data and signaling streams can be packetized orframe-based flows.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer,”“prosumer,” “agent,” and the like are employed interchangeablythroughout the subject specification, unless context warrants particulardistinction(s) among the terms. It should be appreciated that such termscan refer to human entities or automated components supported throughartificial intelligence (e.g., a capacity to make inference based oncomplex mathematical formalisms) which can provide simulated vision,sound recognition and so forth.

The following abbreviations are relevant, at least in part, to thesubject specification.

-   3G Third Generation-   3GPP Third Generation Partnership Project-   AGPS Assisted GPS-   AP Access Point-   BCH Broadcast Channel-   CGI Cell Global Identity-   CN Core Network-   CS Circuit-Switched-   DAS Distributed Antenna System-   DCH Dedicated Transport Channel-   DSL Digital Subscriber Line-   E911 Enhanced 911-   FACH Forward Access Channel-   FL Forward Link-   GGSN Gateway GPRS Service Node-   GPRS General Packet Radio Service-   GSM Global System for Mobile Communication-   GNSS Global Navigation Satellite System-   GW Gateway-   ISDN Integrated Services Digital Network-   UE User Equipment-   IMS IP Multimedia Subsystem-   IP Internet Protocol-   ISP Internet Service Provider-   IPTV IP Television-   NBSP NodeB Site Pair-   PCH Paging Channel-   PCS Personal Communications Service-   PS Packet-Switched-   PSTN Public Switched Telephone Network-   RAN Radio Access Network-   RAT Radio Access Technology-   RBS Radio Base Station-   RL Reverse Link-   RL-TDOA RL Time Difference of Arrival-   RL-TOA RL Time of Arrival-   RNC Radio Network Controller-   RRC Radio Resource Control-   RTT Round Trip Time-   SGSN Serving GPRS Support Node-   TA Timing Advance-   U-TDOA Uplink Time Difference of Arrival-   U-TOA Uplink Time of Arrival-   URA UTRAN Registration Area-   UTRAN Universal Terrestrial Radio Access Network

FIG. 1 is a schematic example wireless environment 100 that can operatein accordance with aspects described herein. In particular, examplewireless environment 100 illustrates a set of wireless network macrocells. Three coverage macro cells 105 ₁-105 ₃ comprise the illustrativewireless environment; however, it should be appreciated that wirelesscellular network deployments can encompass any number of macro cells,for example, 10⁴-10⁵ coverage macro cells. Coverage macro cells 105 _(λ)(λ=1,2,3) are illustrated as hexagons; however, coverage cells can adoptother geometries generally dictated by a deployment configuration orfloor plan, geographic areas to be covered, and so on. Each macro cell105 _(λ) is sectorized in a 2 π/3 configuration in which each macrocells includes three sectors, demarcated with dashed lines in FIG. 1. Itshould be appreciated that other sectorizations are possible, andaspects or features of the disclosed subject matter can be exploitedregardless of type of sectorization. Macro cells 105 ₁, 105 ₂, and 105 ₃are served respectively through NodeB 110 ₁, 110 ₂ and 110 ₃. Any twoNodeBs can be considered a NodeB site pair (NBSP) It is noted that radiocomponent(s) are functionally coupled through links such as cables(e.g., RF and microwave coaxial lines), ports, switches, connectors, andthe like, to a set of one or more antennas that transmit and receivewireless signals (not illustrated). It is noted that a radio networkcontroller (not shown), which can be a part of mobile networkplatform(s) 108, and set of base stations (e.g., Node B 110 _(n), withn=1,2, . . . ) that serve a set of macro cells; electronic circuitry orcomponents associated with the base stations in the set of basestations; a set of respective wireless links (e.g., links 115 _(k) wherek=1,2, . . . ) operated in accordance to a radio technology through thebase stations, form a macro radio access network (RAN). It is furthernoted, that based on network features, the radio controller can bedistributed among the set of base stations or associated radioequipment. In an aspect, for UMTS-based networks, wireless links 115_(λ) embody a Uu interface (UMTS Air Interface).

Mobile network platform(s) 108 facilitates circuit switched (CS)-based(e.g., voice and data) and packet-switched (PS) (e.g., internet protocol(IP), frame relay, or asynchronous transfer mode (ATM)) traffic andsignaling generation, as well as delivery and reception for networkedtelecommunication, in accordance with various radio technologies fordisparate markets. Telecommunication is based at least in part onstandardized protocols for communication determined by a radiotechnology utilized for communication. In addition telecommunication canexploit various frequency bands, or carriers, which include any EMfrequency bands licensed by the service provider (e.g., personalcommunication services (PCS), advanced wireless services (AWS), generalwireless communications service (GWCS), and so forth), and anyunlicensed frequency bands currently available for telecommunication(e.g., the 2.4 GHz industrial, medical and scientific (IMS) band or oneor more of the 5 GHz set of bands). In addition, wireless networkplatform(s) 108 can control and manage base stations 110 _(λ), and radiocomponent(s) associated thereof, in disparate macro cells 105 _(λ) byway of, for example, a wireless network management component (e.g.,radio network controller(s), cellular gateway node(s), etc.) Moreover,wireless network platform(s) can integrate disparate networks (e.g.,femto network(s), Wi-Fi network(s), femto cell network(s), broadbandnetwork(s), service network(s), enterprise network(s), . . . ) Incellular wireless technologies (e.g., 3rd Generation Partnership Project(3GPP) Universal Mobile Telecommunication System (UMTS), Global Systemfor Mobile Communication (GSM)), wireless network platform 108 isembodied in a core network and a set of radio network controllers.

In addition, wireless backhaul link(s) 151 can include wired linkcomponents like T1/E1 phone line; a digital subscriber line (DSL) eithersynchronous or asynchronous; an asymmetric DSL (ADSL); an optical fiberbackbone; a coaxial cable, etc.; and wireless link components such asline-of-sight (LOS) or non-LOS links which can include terrestrialair-interfaces or deep space links (e.g., satellite communication linksfor navigation). In an aspect, for UMTS-based networks, wirelessbackhaul link(s) 151 embodies IuB interface.

It should be appreciated that while exemplary wireless environment 100is illustrated for macro cells and macro base stations, aspects,features and advantages of the disclosed subject matter can beimplemented in microcells, picocells, femto cells, or the like, whereinbase stations are embodied in home-based access points.

Timing of wireless signals must take into consideration the time fromwave signal generation or output at radio equipment a transmitter (e.g.,a UE or NodeB) to detection at a receiver (e.g., a UE or NodeB). Suchtiming includes site timing through link(s) to antenna(s) andpropagation time over the air interface or wireless channel. Timingdelay typically is caused by various source, e.g., mismatches amongelectronic elements and components (e.g., impedance mismatch), straycapacitances and inductances, length of the antenna(s) cable(s) in basestation(s); tower height of base station, whereas timing delay spreadgenerally originates from any signal path scattering, or “signalbounces,” such as multipath, strong reflections, etc.; and the like. Inan aspect of the disclosed subject matter, timing and delay errors canbe compensated for where the errors in delay and timing can bedetermined. Wherein better location measurements beget better timingmeasurements, aspects of the disclosed subject matter can, at least inpart, contribute to improved network performance. Similarly, bettertiming measurements can be employed for better location determination.Further, it is noted that compensation of timing delay can depend onsector coverage, e.g., a first sector can be densely populated while aneighboring sector can include substantial areas of lower populationdensity.

A UE observed time difference, ‘C’, includes both a cell site timingportion, ‘A’, and a RF propagation portion, ‘B’, such that A+B=C.Further, where cell site location and UE location are known, the RFpropagation time, B, can be deduced, e.g., B=(distance between UE andcell site/speed of light). Using the deduced RF propagation time, B, andObserved UE time difference, C, the cell site timing, A, can becalculated, as A=C−B. Site timing, A, is relatively stable over periodsof hours to days for most modern network equipment. Once A isdetermined, C can be measured for additional UEs and the RF propagationtime (i.e., B) for theses additional UEs can be determined by B=C−A. RFpropagation time, B, can then be converted into a distance (e.g.,B*speed of light=distance) and, using multilateration techniques, UEspositions can be identified.

Determining the values of B by geometry can be facilitated by having aknowledge of the location the NodeB and UE. NodeB locations aretypically known with high levels of precision, as these are normallypermanent installations. Further, the location of a particular UE can bedetermined using internal GPS systems (e.g., AGPS, usually to within5-10 meter accuracy). Thus an AGPS enabled UE can facilitate thedetermination of A, as disclosed herein, such that the location ofnon-location aware UEs in a macro cell can be calculated, for example,by multilateration. In experiments, these measurements can producelocation accuracies for non-location aware UEs with median errors of <70m in suburban areas. Multilateration incorporates compounding errors.Further, multilateration is also computationally significant (e.g.,involves hyperbolic functions between NBSPs at (N−1)!, where N is thenumber of cell sites, for example, 5 cell sites would involve 24simultaneous hyperbolic functions.) Timed fingerprint locating (TFL), asdisclosed herein, can reduce computational complexity and providepre-computed values in lookup tables to facilitate improved locationtechniques.

FIG. 2 is a diagram of a subset of wireless devices (e.g., UEs) in atimed fingerprint location (TFL) wireless environment 200, in accordancewith aspects of the disclosed subject matter. TFL wireless environment200 includes a bin grid 210 that is a relative coordinate frameworkwhich defines a matrix of evenly spaced points referred tointerchangeably as bins or frames. The frames can be correlated to ageographic data set, e.g., the bin grid 210 can provide identifiableregions of a predetermined area related to a mapping of a cell site,NodeB, county, county, etc. It can generally be stated that any UE is inone bin grid 210 frame at a given time.

Bin grid 210 frames are of arbitrary size and number. As such, alllevels of frame granularity with regard to TFL techniques are consideredwithin the scope of the present disclosure. However, for simplicity, bingrid 210 (and also 310) frame size will be considered to be 100 metersby 100 meters for the purposes of discussion herein, as this closelymatches current UMTS chip size (e.g., UMTS chip rate is 3.84 MBit/sec,therefore one chip is roughly 260.42 nsec and 78 meters). Additionally,a bin grid can comprise other bin grids or portions thereof. Moreover,bin grids may overlap wholly or partially and at any orientation. Forsimplicity, only single bin grids are discussed herein, but allpermutations are considered within the scope of the present disclosure.It is further noted that a bin grid can be physically two dimensional(2D) or three dimensional (3D), wherein a 2D grid can, for example,include x,y coordinates (e.g., latitude, longitude) and a 3D grid canadd, for example, a z dimension (e.g., height). For simplicity, only 2Dbin grids are discussed herein, although both 2D and 3D bin grids areconsidered within the scope of this disclosure. Moreover, abstractdimensions can be considered, such as, for example, time, network type,subscriber level, etc. All such additional abstract dimensions arewithin the scope of the subject disclosure. For simplicity, theseadditional abstract dimensions will not be further discussed.

TFL wireless environment 200 can further include one or more NodeB cellbase sites 220, 222, 224. These NodeB are typically fixed locations withwell-characterized location coordinates such that the distance between aNodeB and any given frame of bin grid 210 can be easily computed. InFIG. 2, distances 230 and 232 correlate to measurements between NodeB220 and frames 250 and 254 respectively. Similarly, distances 234 and236 correlate to measurements between NodeB 222 and frames 250 and 254respectively. Likewise, distances 238 and 240 correlate to measurementsbetween NodeB 224 and frames 250 and 254 respectively. Given that thedistances from any NodeB to any frame in bin grid 210 can be accuratelycalculated, the differential distance of any frame to any two NodeB canalso be accurately determined. Similarly, the distance between any twoframes of bin grid 210 is readily calculated.

Additionally, TFL wireless environment 200 can include one or more UEs(e.g., mobile phones 260, 262, 264 and 266). These UEs can includelocation aware UEs (e.g., AGPS enabled UE) and non-location aware UEs.Wireless interactions with UEs can be correlated to nodes in bin grid210. For example, an AGPS enabled UE can determine a latitude andlongitude that can be correlated with a bin grid 210 node encompassingthat same latitude and longitude.

Where UE 260 is a location aware UE (e.g., UE 260 is AGPS enabled), UE260 can make location information available to the TFL wirelessenvironment 200, for example, through one or more NodeBs (e.g., 220,222, 224). UE 260, for instance, can be located at a latitude andlongitude within frame 250 of bin grid 210 and, for example, cantransmit this latitude and longitude location information, which can bereceived by, for example, NodeB 220. This latitude and longitudelocation information can be directly correlated to frame 250 and alsowith the RF propagation delay, B, to frame 250 from some NodeB in TFLwireless environment 200. For example, where UE 260 is located in frame250, propagation delay ‘B’ is the distance 230 between from NodeB 220and UE 260 (located in frame 250) divided by the speed of light. Thus,the propagation delay ‘B’ can be directly determined because thelocation of both the NodeB and the UE are known, as disclosed herein.Further, NodeB 220 can communicate this location information,propagation delay information, or derivatives thereof, to otherequipment associated with the wireless network.

Given that the propagation delay, B, can be determined for locationaware UEs within TFL wireless environment 200, cell site delay, ‘A’, canbe determined where the observed UE time difference, C, is alsoavailable. Continuing the present example, UE 260 can make time data(e.g., an observed UE time difference, ‘C’) accessible to the TFLwireless environment 200. Cell site delay (‘A’) can be calculated bysubtracting the propagation delay (‘B’) from the observed UE timedifference (‘C’), (e.g., A=C−B). As discussed herein, A is generallystable over periods of hours to days. Assuming A is relatively stable, Bcan be determined for some C value (e.g., B=C−A). Most or all UEs (e.g.,both location enabled and non-location enabled UEs) can make time data(e.g., an observed UE time difference, ‘C’) available to the TFLwireless environment 200, for example by transmitting it wirelessly,such that the propagation delay for most or all UEs can be determinedwith a simple calculation, given the determined A value. Further, giventhat the propagation delay, B, is essentially proportional to distance(e.g., B*speed of light=distance), the propagation delay can be employedto map out a region of probable locations for a UE at a set distancefrom the related NodeB (e.g., the NodeB from which the propagation delaywas measured). This provides a centroid region in which the UE wouldlikely be located.

It is well understood that errors can be associated with the variousmeasurements involved in the disclosed calculations, for example, AGPSmeasurements are only accurate to about 5-10 meters, an AGPS measurementcan be taken from various positions with a single 100 m×100 m bin gridframe, signals can be bounced, etc.) These errors can be addressed bywell-known statistical methods for sufficiently large sets of locationdata. In an aspect of the disclosed subject matter, a frame of bin grid210 can be selected as a reference frame and measurements within thatframe can be statistically manipulated to improve the value of the datagathered. For example, where UE 260 and UE 262 are both location awareUEs located within from 250, the AGPS and time data from both UE s (260and 262) can be statistically combined and manipulated to increase therelevance of the collected data and the resulting calculated values.Moreover, measurements taken from other frames can be translated to thereference frame to facilitate an increased population of location datato improve statistical data correction processes. This translation canbe accomplished because knowledge of the spatial relationship betweenthe measured frame and reference frame is known. For example, UE 264 cantransmit AGPS location and time data from frame 252. This informationcan be accessed by a NodeB of TFL wireless environment 200. The data canbe manipulated to translate the measured data from frame 252 into theexpected values for UE 264 in frame 250. This can allow the AGPSlocation and time data, collected in various frames of bin grid 210 fromlocation aware UEs, to be correlated to a relevant reference frame. Thisinformation can then be statistically adapted to provide improved datafor use in calculating locations for non-location aware UEs. Theequations for translation among frames will be further disclosed herein.

Differential measurements can be computed for one or more frames of bingrid 210 for any plurality (e.g., pair) of NodeBs within TFL wirelessenvironment 200. For ease of understanding, bin grid 200 can comprise alarge plurality of frames that are each uniquely identified allowing asingle frame to be referenced by a “frame number”. Alternative frameidentification schemes are just as easily applied to identification ofindividual frames, e.g., row/column numbering, region/subregion/sector,etc., and all such schemes are considered within the scope of thepresent disclosure, however, such alternatives will not herein befurther discussed. Similarly, each NodeB in a wireless system can beidentified uniquely by a plurality of conventions, all of which arewithin the scope of the present disclosure, however only a “lettered”index is herein discussed, e.g., site j, site i, site k, etc. Where eachNodeB is uniquely identifiable, each pair of sites is also uniquelyidentifiable, e.g., pair ij, pair ik, pair jk, pair ji, pair ki, pairkj, etc. Further, NBSPs and frames can be identified parenthetically as“(NBSP, frame)”, wherein a question mark, ‘?’, can be used to indicatean arbitrary value, e.g., (ij,250) is the 250 frame in relation to NBSP“ij”, while (ij,?) is some undefined frame related to NBSP “ij”, (?,250)refers to frame 250 in relation to an undefined NBSP, and similarly(?,?) refers to an arbitrary NBSP and arbitrary frame.

For each pair of NodeBs, each frame of a subset of relevant framesassociated with the NBSP can have a value assigned that corresponds tothat frame's differential value. A subset can comprise some frames, allframes, or no frames (e.g., an empty set). A differential value is ageometrically determined value related to the “distance” of a frame froma NBSP, measured in chip (e.g., “distance” can be a temporal or physicaldistance.) For known geometries, differential values can be pre-computedfor frames.

The NBSPs of TFL wireless environment 200 can each have a referenceframe (?,R), for example, (ij,R), (jk,R), (ik,R), etc. An observed timedifference, OV(?,?), can be related to the “C” value reported by alocation aware UE of system 200. In an aspect, an OV(?,R) value can bedirectly obtained by data from location aware UEs at a reference frame,R (e.g., UE 260 or UE 262 at reference frame 250 of TFL wirelessenvironment 200.) In another aspect, where an OV(?,X) value from alocation aware UE is reported from an instant frame, X, other than thereference frame, R, the value can be translated to a reference framevalue based, at least in part, on known differential propagation delaysaccording to:

OV(ji,R)=OV(ji,X)+DV(ij,R)−DV(ij,X),  Eq. (1)

where

X identifies an instant frame, R identifies a reference frame, ijidentifies a NBSP, ji identifies a NBSP, DV(ij,R) is a differentialvalue for the reference frame of the NBSP ij that can be determined fromthe ij NBSP and reference frame geometry, DV(ij,X) is a differentialvalue for frame X for NBSP ij that can be determined from the ij NBSPand frame X geometry, and OV(ji,X) is an observed value for an frame Xof the ji NBSP, such that OV(ji,R) can be calculated. Moreover, OV(?,?)values can be weighted, for example, if a reporting UE is known to bemoving at a high rate of speed, the OV(?,?) can be of less relevance andtherefore can be given a lower weight. These weighted values can bestatistically manipulated to provide a more relevant value for OV(?,R),for example, by reducing measurement error effects.

Whereas DV(ij,R) is the geometrically determined differential value forthe reference frame of the ij NBSP, and having previously determined theobserved time difference value for the reference frame of the ji NBSP asOV(ji,R), Eq. 1 can be manipulated to determine the expected observedtime difference values for any instant frame, X, by simply determiningthe differential value for any instant frame, X, of the ji NBSP,DV(ji,X), and computing:

OV(ji,X)=OV(ji,R)+DV(ij,X)−DV(ij,R).  Eq. (2)

This equation can be similarly employed for other known NBSPs as well.In an aspect, this allows an indexed mapping of expected OV(?,?)value(s) for frame(s) of NBSP(s) given a known geometry for the NBSPsand bin grid, and an OV(?,R) reference cell value (e.g., ascertainedfrom location and time data from one or more location enabled UEsassociated with a NBSP.)

FIG. 3 illustrates differential propagation delay sets for NBSPs in atime fingerprint location wireless environment 300 in accordance withaspects of the subject disclosure. TFL wireless environment 300 can bereferenced to a bin grid 310 and comprise one or more NodeBs (e.g., 320,322, 340). As disclosed herein, the differential values, DV(?,X), can begeometrically determined, and mapped to each frame for every known NBSP.In an aspect, this DV(?,?) mapping can be in the form of a table or setof tables to facilitate rapid access to the data. Further, in a NBSP,for example ij, where an OV(ji,X) value is ascertained and OV(ji,R) isknown, a corresponding DV(ij,X) value can be generated by:

DV(ij,X)=OV(ji,X)−OV(ji,R)+DV(ij,R),  Eq. (3)

which is obtained by simple manipulation of Eq. 2. This calculated valueof DV(ij,X) can function as a lookup value into the tabulated DV(?,X)data set(s), e.g., “return frames for NBSP ij having a DV(ij,X) valueequivalent to the computed value from Eq. 3.” The returned valuesgenerally will form a hyperbola for the NBSP when overlaid on ageographic map of the sites and bin grid framework. For the exemplary ijNBSP, the returned set can be referred to as the “ij primary set” andcan be represented by the set of shaded frames 326 between sites 320 and322, with corresponding ij NBSP reference frame 324.

In an aspect, UEs can be in an environment with a plurality of availableNBSPs, for example, a UE located in frame 360 can be exposed to NBSPs320/322, 320/340, and 322/340. NBSPs can return a related primary set.Prioritization of NBSPs can be beneficial by returning a primary set forthe most relevant NBSP. In accordance with an aspect of the disclosedsubject matter, a NBSP can be prioritized based, at least in part, onreceived signal code power (RSCP) which denotes the power measured by areceiver on a particular physical communication channel of the wirelessnetwork 300. For example, the FIG. 3 the NodeB RSCP values can behighest for 322, moderate for 340, and lowest for 320. Continuing thisnon-limiting example, the ordering of the NodeBs by RSCP can result inlooking up primary sets from pair 322/340 first, then 322/320 second,and 340/320 last. It will be appreciated by one of skill in the art thatother factors and criteria can be employed in determining what NBSPs areemployed for primary pair lookups, for example, confidence scores fordata sets, computational load aspects, number of requests in queue,cached primary sets, etc. It will further be appreciated that thesenumerous other factors and criteria are all within the scope of thepresent disclosure. As an additional non-limiting example, where NodeB320 has recently come online and has a relatively newer data set ascompared to 322 and 340, there may be a lower confidence in using thosevalues and priority can be given to other NBSPs for lookup.

In an aspect, determining a DV(?,X) can be related to calculating alocation for a UE time data (“C”) accessed for a non-location enabled UEwithout the mathematical complexity of hyperbola multilateration. Asdisclosed herein, where “A” is relatively stable over hours or days, and“C” is accessed for a UE, the “B” value can be calculated and is relatedto the DV (in units of chip) from any pair of NodeBs. When UE time datais accessed, a DV(?,X) look-up can be initiated. Relevant NBSPs can beprioritized as part of the look-up, for example, by RSCP, etc. Further,the relevant pairs can be employed as an index to lookup a first primaryset. As an example, in FIG. 3, time data for a UE (not illustrated) canbe accessed in relation to a locating event in TFL wireless environment300. In this example, it can be determined that NBSP 322/340, withreference frame 342, be used for primary set lookup with the computedDV(?,X) value as the index. This can for example return the shadedframes 344 forming a hyperbola between NodeB 322 and NodeB 340 whereDV(?,X)=DV(322/340,X). This indicates that the UE is most likely locatedat one of the shaded frames of set 344. A second lookup can then beperformed for an additional relevant NBSP, for example, NBSP 320/322,with reference frame 324, using the same value DV(?,X), as an index intothe data set. Continuing the example, the returned set for the look upwith DV(320/322,X)=DV(?,X) can return the set of shaded frames 326.Thus, the UE is also most likely located in the shaded frames designatedby set 326. Therefore, where the UE is most likely in both set 344 andset 326, it is clear that the most probable location for the UE is atthe intersection of the two sets, at frame 360.

It will be appreciated that additional lookups can be undertaken in anattempt to isolate the most likely frame by intersections of returnedsets and that this aspect is within the scope of the current disclosure.In a further aspect, where no exact match is found, frames within apredetermined distance of the closest match can be retained as eligible,for example, frames within 5 chips of the closest match. In anotheraspect, more or less granular data sets can be employed to give morerefined location results, for example, a more coarse granularity can beemployed so that a match can be obtained even where the locationfootprint area is larger. As a second example, a finer granularity canbe employed to better determine the location of a UE (e.g., a smallerfootprint area) where such a data set is available and an exact matchframe results. In some instances no single frame can be resolved (e.g.,insufficient data, too few NodeBs, etc.)

FIG. 4 illustrates a block diagram of an exemplary system 400 thatfacilitates time fingerprint location in accordance with aspectsdescribed herein. In an aspect, system 400 can be a part of a mobilenetwork, and can reside at least in part within at least one of anetwork management component (e.g., radio network controller, corenetwork, or network gateway) or a base station or access point. Examplesystem 400 includes a time fingerprint location (TFL) platform 410 thatfacilitates location of UEs based at least in part on receiving anobserved timing offset related to a frame.

Calibration platform 410 includes a bin grid framing (BGF) component 412that can estimate the location of a mobile device or a stationary devicethat can communicate wireles sly. To at least that end, BGF component412 can include a bin grid (BG) model selector 414 that selects anappropriate bin grid based at least in part on the geometry of the NBSPsinteracting with the UE (not shown), e.g., selecting a particular subsetof a bin grid to include frames related to NBSPs relevant to locatingone or more UEs transmitting time data, select from bin grids ofdifferent granularity, selecting bin grid models related to updatingframe data, etc. The model can be utilized in conjunction with observedtime difference values (‘C’), propagation timing values (‘B’), and sitetiming values (‘A’) among forward link (FL) and reverse link (RL)wireless signals, e.g., signaling or traffic, delivered and received,respectively, by a base station or access point.

In an aspect, in example system 400, TFL platform 410 can requestinformation (e.g., timing data, location data from a location enabledUE, etc.) from UEs through a FL wireless signal 436 that is conveyed bya signaling and traffic component 434 in a communication platform 430,which can be a part of a serving access point or NodeB. System 400 canaccess the requested information for UEs on a RL wireless signal 438, inthe RL counterpart transport channel. It should be appreciated thatcommunication platform 430 includes antenna(s) 432.

As described above, BGF component 412 can estimate location of a mobiledevice by at least in part searching computed location data sets forNBSPs and timing data that correlates to the computed values. The clocklayer 416 can facilitate determining the propagation delay (‘B’), forexample from accessing an observed time difference (‘C’). It is notedthat such returned timing data is typically part of basic, conventionalUE RAN operation, and no additional equipment is necessary to generatesuch data in most cases. Where such equipment is needed, it can beincluded and should be considered within the scope of the presentlydisclosed subject matter. Returned timing data in conjunction with bingrid frameworks provide a location estimate. Calibration platform 410also includes analysis component 418 that can implement variousalgorithms, stored in algorithm storage 444, to characterize or evaluatevarious features of the returned location data, location estimates,etc., generated by BGF component 412. In an aspect, algorithms employedby analysis component 418 include statistical analysis methodologies;other analysis methodologies such as spectral analysis and time-seriesanalysis also can be utilized. Location data can be cached in framelocation storage 432. Frame location storage can be communicativelycoupled to other data storage locations (not illustrated), eitherlocally or remotely, to facilitate sharing and updating of the framelocation information.

In an aspect, system 400 can facilitate compensation of wireless signal(e.g., RF, microwave, infrared . . . ) timing variations, or correctionof wireless signal propagation information by way of TFL platform 410.TFL platform 410 can access location and time data from location awareUEs (e.g., as part of UEs 420) and time data from non-location aware UEs(e.g., as part of UEs 420). This timing and/or location data can be madeavailable to data management component 411. Accessed location and/ortiming data 415 can be retained in frame location storage 442 as rawdata, processed data, data converted into frame data, etc. It should beappreciated that based upon specific aspects of the UEs 220, TFLplatform 410 can access location and/or timing data 415 over anair-interface by way of communication platform 430, or through a networkmanagement component such as a network server, a radio networkcontroller, or a network gateway. UEs 420 can provide location and/ortiming data based, at least in part, on GNSS, such as assisted GPS, andnetwork planning information. In an aspect, the UEs 420 comprise a setof mobile devices that, at least in part, support GNSS data receptionand manipulation thereof. For example, these UEs can communicate with aGNSS system (e.g., GPS, Galileo, GLONASS . . . ) through a deep-spacelink. These UEs can receive timing signaling that allows determination,at least in part, of accurate position of each UE that receivessufficient information (e.g., timing information from three or moresatellites) for triangulation. Alternatively, UEs can receive assistedtiming information from mobile network platform(s), through basestations serving a relevant sector, mobile network platform(s) receivedtiming information from GNSS through deep-space links, etc. Such timinginformation or location information can be received at various timeinstants and aggregated at TFL platform 410 through analysis component418. Aggregation collects location and timing data received at variousinstants in time in order to augment the statistical significance ofdata and analysis thereof, which can improve accuracy of extractedlocation determinations and related propagation delay data.

FIG. 5 depicts additional or alternative timing and/or location data 415generation components 520 ₁-520 ₃ that are not UEs 420 (e.g.,calibration beacons, femto or pico cell equipment, etc.) to facilitatecalibration of reference frames in a bin grid framework (e.g., 210, 310,etc.). Known locations for a set of probes, or wireless beacons,deployed within a coverage cell or sector, as illustrated, can beemployed to determine A and B in a manner similar to a location awareUE. For example, macro coverage cell 500 is divided in three sectors(demarcated by dashed lines) served by base station 510, wherein asector includes a set of three probes 520 ₁-520 ₃ located at specificpositions that are known, or available, to the one or more networkcomponents (e.g., mobile network platform(s) 108). Probes 520 ₁-520 ₃also communicate with base station 510 through wireless links 515.Communicated time data and a known position of the probes 520 ₁-520 ₃allows calculation of the A and B values as disclosed herein. Wirelessprobes, or beacons, can be stationary or pseudo-stationary. In anexample, wireless probes can be Wi-Fi outdoor access points that arepart of a public metropolitan network. In another example, wirelessprobes can be part of wireless-enabled utility equipment (e.g., electricmeter(s)) deployed within a utility network, e.g., electric grid. Itshould be appreciated that wireless beacons embodied in utility meterscan be better suited for smaller, urban coverage sectors, sincetransmission power of such probes can be low compared to power formWi-Fi access points.

Various aspects of the disclosed subject matter can be automated throughartificial intelligence (AI) methods to infer (e.g., reason and draw aconclusion based upon a set of metrics, arguments, or known outcomes incontrolled scenarios) suitable models for propagation of wirelesssignal, e.g., RF signal, microwave signal, etc.; optimal or near-optimalpositions for probes that enable generation of accurate locationestimates; or the like. Artificial intelligence techniques typicallyapply advanced mathematical algorithms—e.g., decision trees, neuralnetworks, regression analysis, principal component analysis (PCA) forfeature and pattern extraction, cluster analysis, genetic algorithm, orreinforced learning—to a data set; e.g., the collected subscriberintelligence in the case of subscriber segmentation. In particular, oneof numerous methodologies can be employed for learning from data andthen drawing inferences from the models so constructed. For example,Hidden Markov Models (HMMs) and related prototypical dependency modelscan be employed. General probabilistic graphical models, such asDempster-Shafer networks and Bayesian networks like those created bystructure search using a Bayesian model score or approximation also canbe utilized. In addition, linear classifiers, such as support vectormachines (SVMs), non-linear classifiers like methods referred to as“neural network” methodologies, fuzzy logic methodologies also can beemployed.

In view of the example system(s) described above, example method(s) thatcan be implemented in accordance with the disclosed subject matter canbe better appreciated with reference to flowcharts in FIG. 6-FIG. 9. Forpurposes of simplicity of explanation, example methods disclosed hereinare presented and described as a series of acts; however, it is to beunderstood and appreciated that the claimed subject matter is notlimited by the order of acts, as some acts may occur in different ordersand/or concurrently with other acts from that shown and describedherein. For example, one or more example methods disclosed herein couldalternatively be represented as a series of interrelated states orevents, such as in a state diagram. Moreover, interaction diagram(s) mayrepresent methods in accordance with the disclosed subject matter whendisparate entities enact disparate portions of the methodologies.Furthermore, not all illustrated acts may be required to implement adescribed example method in accordance with the subject specification.Further yet, two or more of the disclosed example methods can beimplemented in combination with each other, to accomplish one or morefeatures or advantages herein described. It should be furtherappreciated that the example methods disclosed throughout the subjectspecification are capable of being stored on an article of manufactureto allow transporting and transferring such methods to computers forexecution, and thus implementation, by a processor or for storage in amemory.

FIG. 6 presents a flowchart of an example method 600 for determining alocation for a UE. The location determination can facilitate correctingRF propagation delay information in an operational wireless systemaccording to aspects described herein. Further, location information canbe leveraged to provide location-centric information and services tousers associated with UEs, for example, maps, events, sales, or socialnetworking services, etc. Location-centric information and services canalso be employed to provide additional services and products by way ofaggregation of location information, for example, traffic data, usagedata, or demographic data, among others. Still further, certain locationdependant services can leverage location determinations of UEs, forexample, E911. The subject example method 600, while illustrated for RFsignal, also can be employed with regard to electromagnetic radiation(EM) with frequencies other than radio frequency, for instance,microwave EM radiation, infrared radiation, etc. In an aspect, thesubject example method 600 can be implemented by one or more networkcomponents, e.g., TFL platform 410. Alternatively or additionally, aprocessor (e.g., processor 450) configured to confer, and that confers,at least in part, functionality to the one or more network componentscan enact the subject example method 600. At 610, an OV(?,R) value isdetermined. The determined OV(?,R) value can be based, at least in part,on a time difference between a NBSP in the TFL wireless network and aUE. In an aspect, the OV(?,R) can be determined by solving Eq. 1 whereDV(?,R) and DV(?,X) can be determined based on the geographical locationof the reference frame R and the instant frame X from the relevant NBSP,and where the received time difference is OV(?,X). Further, as describedherein, in an aspect, where a location aware UE is available to providedata for the reference frame R, OV(?,R) can be calculated directly asequivalent to the OV(?,X) because X =R under these conditions. Further,location aware UEs that can support reception of GNSS data, such asassisted GPS (AGPS), and operation thereon (e.g., injection of GNSS dataon location based applications that execute, or are native, to themobile) or manipulation thereof, such as delivery of location data, caneasily provide access to this location information. This locationinformation can facilitate rapid geographical location of an instantframe X of the TFL wireless network, such that the DV(?,?) values can berapidly calculated and employed in translating OV(?,X) values intoOV(?,R) values. Additionally, location determinations can beaccumulated, weighted, and/or otherwise statistically manipulated toprovide improvements to the resulting value, for example, averaging overa plurality of OV(?,R) can be employed to reduce certain types of errorpropagation. In a further aspect, the location information can beaccessed through network components that retain known locations, forexample, location probes or wireless beacons (e.g., probes 520 ₁-520 ₃).As an example, wireless beacons can be fixed location Wi-Fi outdooraccess points that are part of a public metropolitan network.

At 620, the differential value DV(?,R) can be determined for thereference frame R. This can be done as part of the determination ofOV(?,R) at 610, or in circumstances where a DV(?,R) is not determined aspart of 610, it can be determined separately at 620, for example, wherea location aware UE facilitated determining OV(?,R) directly at 610,DV(?,R) can be determined separately at 620.

At 630, a differential value DV(?,?) can be determined for frames of abin grid framework and for NBSPs of relevance. Given that NBSP (e.g., afirst and second NodeB) locations are typically well defined physicallocations, the differential value, DV(?,?), can be geometricallydetermined (measured in chips) because the geographic location of eachframe is defined by the bin grid framework in relation to each relevantNBSP. In an aspect, a NBSP can be associated with a limited set ofspecific frames for which the NBSP is relevant. For example, a NBSPlocated in San Diego, Calif. would not be relevant to bin grid frames inRedding, Calif. Thus, the relevant frames can be limited to those ofsignificant value to any specific NBSP. As a non-limiting example, eachNBSP can be limited to an arbitrary number of the closest frames, forexample, 4096 frames. This can serve to reduce data that would otherwisebe of little value.

At 640, a database of bin grid frame locations can be updated withDV(?,?) values for each relevant NBSP. These values can be an indexthrough the tabulated data. These indexes can be employed to findrelevant frame locations, for example, by using an SQL-type query on theindexed data. Where large volumes of data are developed for one or morebin grid frameworks, these datasets can be indexed for fast searchingand can be stored in subsets for particular areas or NBSPs. For example,a bin grid frame location data set can include NBSP indices for a city,county, state, region, country, etc. As another non-limiting example, adata set for a first NodeB and other NodeBs that pair with the firstNodeB can be stored at the first NodeB to facilitate indexed look-ups offrame locations for any NBSP involving the first NodeB. Further, forexample, larger data sets having a plurality of NBSPs (e.g., city wide,regional, etc.) can, for example, include separate NBSP indices as a wayof rapidly traversing data for a frame location look-up.

In an aspect, statistical analysis of the data, e.g., frame locationsand DV(?,?) index values are utilized to establish a correlation betweenpropagation values for multiple NBSPs (e.g., a differential locations)to facilitate structured data analysis that returns a match or limitedset of potential matches for a UE location. These location values can beconverted into propagation times because of the measurements are inchips. This can allow for correction and compensation of wirelessnetwork timing values as well as allowing for location-centric servicesand information aggregation. It should be appreciated that statisticalmetrics can also be employed to quantify a correlation among locationdata information, particularly in aggregated location data applications.

At act 650, a differential value, DV(?,X), for a frame X can bedetermined. For example, Eq. 3 can be employed to determine DV(?,X) fora given OV(?,X) where DV(?,R) and OV(?,R) have already been determined.At 660, a frame location set can be found and returned from the databasebased, at least in part, on DV(?,X) and the relevant NBSP as indexes. Atthis point method 600 can end. As a non-limiting example, for any givenNBSP and index DV(?,X) value, 150 frames with matching DV(?,?) valuescan be returned. This indicates that the UE with the DV(?,X) value islikely located in one of the 150 frames returned. These frames typicallycorrespond to a hyperbola between the NodeBs of the indicated NBSP. Oneadvantage is that the index values for the frames are pre-computed andcomplex math is not required at lookup to get the resulting set as wouldbe required in a traditional multilateration technique. The value of thepre-computation and lookup aspect of the disclosed subject matterbecomes significantly more prominent when numerous NBSPs are searchedfor the same DV(?,X) value. The increase in complexity for traditionalmultilateration techniques is factorial and quickly becomescomputationally intensive. In contrast, the lookup technique remainscomparatively computationally simple, even over large sets of data. Asan example, a relevant set of NBSP frame locations for a given DV(?,X)value is likely to intercept another frame location set for a differentrelevant NBSP in a limited number of frame locations. This can rapidlyresult in convergence on a singular frame location of the two or moresets without the need for any complex math at the time of lookup.

It is noted that the subject example method 600 can be employed forlocation of UE in a TFL wireless network (e.g., 200, 300) andcompensation of RF signal propagation delay in various operationalwireless system such as macro coverage wireless systems; radar systems;home-based wireless systems, e.g., micro cell, pico cell, femto cell,Wi-Fi hot spot; or the like. It should be appreciated that for thevarious aforementioned wireless technologies, propagation of RFsignal(s), microwave signal(s), infrared signal(s), or any otherradiation signal(s), is implemented by a radio communication component,e.g., signaling and traffic component 434, that can reside within anaccess point that operates in accordance with a respective wirelesstechnology.

FIG. 7 is a flowchart of an exemplary method 700 for iterativelydetermining a location of a UE in a TFL wireless network according toaspects of the disclosed subject matter. The subject example method 700can be implemented by one or more network components, e.g., TFL platform410. Alternatively or additionally, a processor (e.g., processor 450)configured to confer, and that confers, at least in part, functionalityto the one or more network components can enact the subject examplemethod 700.

At 710, a database of frame locations is updated with differentiallocation values and NBSP identification values. Updating the DV(?,?)indices and the NBSP indices for frame location keeps the dataset wellmaintained over time as values can drift and NBSPs can change as sitesare commissioned, decommissioned, taken down for service, or return fromservice, for example. Where a NodeB, for example, unexpectedly goesoffline, all existing DV(?,?) values for NBSPs including the offlineNodeB will be immediately invalid. Hence, updates to the databaserecords to, for example, remove NBSPs attached to the offline NodeB willhelp to keep location lookups in that region relevant and accurate.

At 720, a differential location value for a UE can be determined. Thiscan be calculated from the time data transmitted by the UE by way of Eq.3 where the remaining values are known. Further, a first NBSP can bedetermined as a database index term. For example, RSCP values canindicate the closest NBSPs from which the most relevant set can beselected as a first index. Other examples of selecting relevant NBSPscan include selecting sites that have statistically reliable dataassociated with them, selecting pairs that are related to a generaldirection of travel, availability of look-up resources at variousnetwork components, etc.

At 730, a set of frame locations is returned from the database search ofthe selected NBSP and DV(?,X) value searched. As disclosed herein, thisset can include zero, one, many, or all frame locations. As anon-limiting example, a search can return 150 frame locations in theframe location set.

At 740, for each iteration, only frames in both the current and previousset are retained. Whereas it is the first iteration, all frames in theset are kept.

At 750, a determination is made based on the number of frame locationsin the frame location set. Where there is more than one frame in theframe location set, the method passes to 760. Where there is only oneframe in the set, the method 700 proceeds to 770.

At 760, the NBSP is incremented and the method 700 returns to 730. Uponthe return to 730, the DV(?,X) value is searched again but his timeindexed with the next relevant NBSP. The method at 730 returns a newframe location set and proceeds to 740. At 740 for the second iteration,the frames of the new set and the preceding set are compared and framesfound in both sets are retained in the frame set, such that uponsubsequent iterations, this retained set is compared to subsequent newframe location sets and a single value will be converged on iteratively.At 750, a determination is again made relating to the number of framelocations in the frame location set. This process continues until asingle frame location is present in the frame location set. One of skillin the art will appreciate that non-convergent behavior is not discussesherein, but that such behavior is within the scope of the disclosure.For example, where the behavior is non-convergent, the method can endwithout satisfying the decision at 750.

At 770, the sole remaining frame location is equated to the location ofthe UE and the UE location is updated to reflect this determination. Atthis point method 700 can end.

FIG. 8 presents a flowchart of an example method 800 for updating sitetiming values according to aspects of the disclosed subject matterdescribed herein. The subject example method 800 can be implemented byone or more network components, e.g., TFL platform 410. Alternatively oradditionally, a processor (e.g., processor 450) configured to confer,and that confers, at least in part, functionality to the one or morenetwork components can enact the subject example method 800.

At 810, an OV(?,R) update value can be determined. This can be based, atleast in part, on a location aware UE providing an updated observedtiming difference, e.g., OV(?,X), and location data. From this, OV(?,X)can be translated to OV(?,R), as disclosed previously herein, by Eq. 1.

At 820, the determined OV(?,R) update value can be weighted and combinedwith the then current value of OV(?,R). This can provide additionalrobustness to the reference frame observed value, which relates to sitetiming in the wireless network as disclosed herein. One example of thistype of updating is weighted averaging, which can reduce randommeasurement errors. One of skill in the art will appreciate thatnumerous other types of statistical transformations can be applied toimprove the quality of the measured value in light of subsequentmeasurements, all such manipulations are believed to be within the scopeof the presently disclosed subject matter.

At 830, the updated OV(?,R) value is employed while not crossing athreshold value. Additional updates to the OV(?,R) can be accomplishedby looping back to 810 from 830, as illustrated, even where no thresholdvalue has been exceeded. An example of this type of OV(?,R) update valuefrom 830 can include a location aware UE coming into a network andproviding additional OV(?,X) data and location data such that an OV(?,R)update can be calculated and incorporated. At this point method 800 canend.

At 840, where a threshold value has been crossed, the existing OV(?,R)value can be purged and a new value can be applied. In an aspect thisnew OV(?,R) value can be based on data from location aware UE reports.In another aspect, this value can be a previously stored value from aknown operational state. The purging and replacement of this value canoccur, for example, where NBSP conditions have drastically changed andthe existing site timing information is significantly error prone. Anexample of conditions that might precipitate this behavior can includeequipment failure, natural disaster, construction, etc. From 840, method800 can return to 810 and iteratively update the new value of OV(?,R) asherein described. At this point method 800 can end.

FIG. 9 illustrates a block diagram of an example embodiment of an accesspoint to implement and exploit one or more features or aspects of thedisclosed subject matter. In embodiment 900, AP 905 can receive andtransmit signal(s) (e.g., attachment signaling) from and to wirelessdevices like femto access points, access terminals, wireless ports androuters, or the like, through a set of antennas 920 ₁-920 _(N) (N is apositive integer). It should be appreciated that antennas 920 ₁-920 _(N)embody antenna(s) 432, and are a part of communication platform 915,which comprises electronic components and associated circuitry thatprovides for processing and manipulation of received signal(s) andsignal(s) to be transmitted. Such electronic components and circuitryembody at least in part signaling and traffic component 434;communication platform 915 operates in substantially the same manner ascommunication platform 430 described hereinbefore. In an aspect,communication platform 915 includes a receiver/transmitter 916 that canconvert signal (e.g., RL signal 438) from analog to digital uponreception, and from digital to analog upon transmission. In addition,receiver/transmitter 916 can divide a single data stream into multiple,parallel data streams, or perform the reciprocal operation. Coupled toreceiver/transmitter 916 is a multiplexer/demultiplexer 917 thatfacilitates manipulation of signal in time and frequency space.Electronic component 917 can multiplex information (data/traffic andcontrol/signaling) according to various multiplexing schemes such astime division multiplexing (TDM), frequency division multiplexing (FDM),orthogonal frequency division multiplexing (OFDM), code divisionmultiplexing (CDM), space division multiplexing (SDM). In addition,mux/demux component 917 can scramble and spread information (e.g.,codes) according to substantially any code known in the art; e.g.,Hadamard-Walsh codes, Baker codes, Kasami codes, polyphase codes, and soon. A modulator/demodulator 918 is also a part of communication platform915, and can modulate information according to multiple modulationtechniques, such as frequency modulation, amplitude modulation (e.g.,M-ary quadrature amplitude modulation (QAM), with M a positive integer),phase-shift keying (PSK), and the like. Communication platform 915 alsoincludes a coder/decoder (codec) component 919 that facilitates decodingreceived signal(s), and coding signal(s) to convey.

Access point 905 also includes a processor 935 configured to conferfunctionality, at least in part, to substantially any electroniccomponent in AP 905. In particular, processor 935 can facilitatedetermination of propagation delay information of RF signal, ormicrowave signal, among communication platform 915 and antennas 920₁-920 _(N) in accordance with various aspects and embodiments disclosedherein. Power supply 925 can attach to a power grid and include one ormore transformers to achieve power level that can operate AP 905components and circuitry. Additionally, power supply 925 can include arechargeable power component to ensure operation when AP 905 isdisconnected from the power grid, or in instances, the power grid is notoperating.

Processor 935 also is functionally connected to communication platform915 and can facilitate operations on data (e.g., symbols, bits, orchips) for multiplexing/demultiplexing, such as effecting direct andinverse fast Fourier transforms, selection of modulation rates,selection of data packet formats, inter-packet times, etc. Moreover,processor 935 is functionally connected, via a data or system bus, tocalibration platform 912 and other components (not shown) to confer, atleast in part functionality to each of such components.

In AP 905, memory 945 can store data structures, code instructions andprogram modules, system or device information, code sequences forscrambling, spreading and pilot transmission, location intelligencestorage, determined delay offset(s), over-the-air propagation models,and so on. Processor 935 is coupled to the memory 945 in order to storeand retrieve information necessary to operate and/or conferfunctionality to communication platform 915, calibration platform 912,and other components (not shown) of access point 905.

FIG. 10 presents an example embodiment 1000 of a mobile network platform1010 that can implement and exploit one or more aspects of the disclosedsubject matter described herein. Generally, wireless network platform1010 can include components, e.g., nodes, gateways, interfaces, servers,or disparate platforms, that facilitate both packet-switched (PS) (e.g.,internet protocol (IP), frame relay, asynchronous transfer mode (ATM))and circuit-switched (CS) traffic (e.g., voice and data), as well ascontrol generation for networked wireless telecommunication. Mobilenetwork platform 1010 includes CS gateway node(s) 1012 which caninterface CS traffic received from legacy networks like telephonynetwork(s) 1040 (e.g., public switched telephone network (PSTN), orpublic land mobile network (PLMN)) or a signaling system #7 (SS7)network 1070. Circuit switched gateway node(s) 1012 can authorize andauthenticate traffic (e.g., voice) arising from such networks.Additionally, CS gateway node(s) 1012 can access mobility, or roaming,data generated through SS7 network 1070; for instance, mobility datastored in a visited location register (VLR), which can reside in memory1030. Moreover, CS gateway node(s) 1012 interfaces CS-based traffic andsignaling and PS gateway node(s) 1018. As an example, in a 3GPP UMTSnetwork, CS gateway node(s) 1012 can be realized at least in part ingateway GPRS support node(s) (GGSN). It should be appreciated thatfunctionality and specific operation of CS gateway node(s) 1012, PSgateway node(s) 1018, and serving node(s) 1016, is provided and dictatedby radio technology(ies) utilized by mobile network platform 1010 fortelecommunication.

In the disclosed subject matter, in addition to receiving and processingCS-switched traffic and signaling, PS gateway node(s) 1018 can authorizeand authenticate PS-based data sessions with served mobile devices. Datasessions can include traffic, or content(s), exchanged with networksexternal to the wireless network platform 1010, like wide areanetwork(s) (WANs) 1050, enterprise network(s) 1070, and servicenetwork(s) 1080, which can be embodied in local area network(s) (LANs),can also be interfaced with mobile network platform 1010 through PSgateway node(s) 1018. It is to be noted that WANs 1050 and enterprisenetwork(s) 1060 can embody, at least in part, a service network(s) likeIP multimedia subsystem (IMS). Based on radio technology layer(s)available in technology resource(s) 1017, packet-switched gatewaynode(s) 1018 can generate packet data protocol contexts when a datasession is established; other data structures that facilitate routing ofpacketized data also can be generated. To that end, in an aspect, PSgateway node(s) 1018 can include a tunnel interface (e.g., tunneltermination gateway (TTG) in 3GPP UMTS network(s) (not shown)) which canfacilitate packetized communication with disparate wireless network(s),such as Wi-Fi networks.

In embodiment 1000, wireless network platform 1010 also includes servingnode(s) 1016 that, based upon available radio technology layer(s) withintechnology resource(s) 1017, convey the various packetized flows of datastreams received through PS gateway node(s) 1018. It is to be noted thatfor technology resource(s) 1017 that rely primarily on CS communication,server node(s) can deliver traffic without reliance on PS gatewaynode(s) 1018; for example, server node(s) can embody at least in part amobile switching center. As an example, in a 3GPP UMTS network, servingnode(s) 1016 can be embodied in serving GPRS support node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)1014 in wireless network platform 1010 can execute numerous applications(e.g., location services, online gaming, wireless banking, wirelessdevice management . . . ) that can generate multiple disparatepacketized data streams or flows, and manage (e.g., schedule, queue,format . . . ) such flows. Such application(s) can include add-onfeatures to standard services (for example, provisioning, billing,customer support . . . ) provided by wireless network platform 1010.Data streams (e.g., content(s) that are part of a voice call or datasession) can be conveyed to PS gateway node(s) 1018 forauthorization/authentication and initiation of a data session, and toserving node(s) 1016 for communication thereafter. In addition toapplication server, server(s) 1014 can include utility server(s), autility server can include a provisioning server, an operations andmaintenance server, a security server that can implement at least inpart a certificate authority and firewalls as well as other securitymechanisms, and the like. In an aspect, security server(s) securecommunication served through wireless network platform 1010 to ensurenetwork's operation and data integrity in addition to authorization andauthentication procedures that CS gateway node(s) 1012 and PS gatewaynode(s) 1018 can enact. Moreover, provisioning server(s) can provisionservices from external network(s) like networks operated by a disparateservice provider; for instance, WAN 1050 or Global Positioning System(GPS) network(s) (not shown). Provisioning server(s) can also provisioncoverage through networks associated to wireless network platform 1010(e.g., deployed and operated by the same service provider), such asfemto cell network(s) (not shown) that enhance wireless service coveragewithin indoor confined spaces and offload RAN resources in order toenhance subscriber service experience within a home or businessenvironment. Server(s) 1014 can embody, at least in part, TFL platform410 and any component(s) therein

It is to be noted that server(s) 1014 can include one or more processorsconfigured to confer at least in part the functionality of macro networkplatform 1010. To that end, the one or more processor can execute codeinstructions stored in memory 1030, for example. It is should beappreciated that server(s) 1014 can include a content manager 1015,which operates in substantially the same manner as describedhereinbefore.

In example embodiment 1000, memory 1030 can store information related tooperation of wireless network platform 1010. In particular, memory 1030can include contents of memory 440 in example system 400. Otheroperational information can include provisioning information of mobiledevices served through wireless platform network 1010, subscriberdatabases; application intelligence, pricing schemes, e.g., promotionalrates, flat-rate programs, couponing campaigns; technicalspecification(s) consistent with telecommunication protocols foroperation of disparate radio, or wireless, technology layers; and soforth. Memory 1030 can also store information from at least one oftelephony network(s) 1040, WAN 1050, enterprise network(s) 1060, or SS7network 1070.

It is to be noted that aspects, features, or advantages of the disclosedsubject matter described in the subject specification can be exploitedin substantially any wireless communication technology. For instance,Wi-Fi, WiMAX, Enhanced GPRS, 3GPP LTE, 3GPP2 UMB, 3GPP UMTS, HSPA,HSDPA, HSUPA,GERAN, UTRAN, LTE Advanced. Additionally, substantially allaspects of the disclosed subject matter as disclosed in the subjectspecification can be exploited in legacy telecommunication technologies;e.g., GSM. In addition, mobile as well non-mobile networks (e.g.,internet, data service network such as internet protocol television(IPTV)) can exploit aspects or features described herein.

Various aspects or features described herein can be implemented as amethod, apparatus or system, or article of manufacture using standardprogramming or engineering techniques. In addition, various aspects orfeatures disclosed in the subject specification also can be effectedthrough program modules that implement at least one or more of themethods disclosed herein, the program modules being stored in a memoryand executed by at least a processor. Other combinations of hardware andsoftware or hardware and firmware can enable or implement aspectsdescribed herein, including disclosed method(s). The term “article ofmanufacture” as used herein is intended to encompass a computer programaccessible from any computer-readable device, carrier, or media. Forexample, computer readable media can include but are not limited tomagnetic storage devices (e.g., hard disk, floppy disk, magnetic strips. . . ), optical discs (e.g., compact disc (CD), digital versatile disc(DVD), blu-ray disc (BD). . . ), smart cards, and flash memory devices(e.g., card, stick, key drive . . . ).

As it employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to comprising, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Processors can exploit nano-scale architectures suchas, but not limited to, molecular and quantum-dot based transistors,switches and gates, in order to optimize space usage or enhanceperformance of user equipment. A processor may also be implemented as acombination of computing processing units.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can include both volatile andnonvolatile memory.

By way of illustration, and not limitation, nonvolatile memory caninclude read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable ROM (EEPROM), or flashmemory. Volatile memory can include random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), anddirect Rambus RAM (DRRAM). Additionally, the disclosed memory componentsof systems or methods herein are intended to comprise, without beinglimited to comprising, these and any other suitable types of memory.

What has been described above includes examples of systems and methodsthat provide advantages of the disclosed subject matter. It is, ofcourse, not possible to describe every conceivable combination ofcomponents or methodologies for purposes of describing the disclosedsubject matter, but one of ordinary skill in the art may recognize thatmany further combinations and permutations of the claimed subject matterare possible. Furthermore, to the extent that the terms “includes,”“has,” “possesses,” and the like are used in the detailed description,claims, appendices and drawings such terms are intended to be inclusivein a manner similar to the term “comprising” as “comprising” isinterpreted when employed as a transitional word in a claim.

What is claimed is:
 1. A method, comprising: receiving, by a systemcomprising a processor, a first observed time value associated withradio links between a first pair of radio devices of a wireless networkand a user equipment; receiving, by the system, a data set comprisinglocation information correlated to a first reference differential timevalue related to the first pair of radio devices, and a first referenceobserved time value associated with the first pair of radio devices;determining, by the system, a first differential time value based on thefirst observed time value; and determining, by the system, locationinformation from the data set based on the first differential time valuewherein the location information facilitates determining a location ofthe user equipment.
 2. The method of claim 1, wherein the firstreference observed time value is associated with the user equipment inthe wireless network, wherein the user equipment also employs anotherlocation determination technique to determine a location for the userequipment.
 3. The method of claim 1, wherein the first referenceobserved time value is associated with a global positioning systemenabled user equipment in the wireless network.
 4. The method of claim1, wherein the first observed time value is based on a first propagationtime for a radio signal between a first radio device of the first pairof radio devices and the user equipment, a second propagation time for aradio signal between a second radio device of the first pair of radiodevices and the user equipment, a first delay time associated with thefirst radio device of the first pair of radio devices, and a seconddelay time associated with the second radio device of the first pair ofradio devices.
 5. The method of claim 1, further comprising:determining, by the system, other location information of the data setbased on another differential time value related to another pair ofradio devices of the wireless network and the user equipment; anddetermining, by the system, a candidate location for the location of theuser equipment based on the location information and the other locationinformation.
 6. The method of claim 5, wherein the determining thecandidate location comprises comparing candidate locations comprisingthe location information to other candidate locations comprising theother location information to reduce a number of candidate locationsbased on common candidate locations of the candidate locations and theother candidate locations.
 7. The method of claim 6, wherein thecandidate locations comprising the location information are bin gridframes and the other candidate locations comprising the other locationinformation are bin grid frames and the determining the candidatelocation comprises determining a union between the bin grid frames ofthe location information and the bin grid frames of the other locationinformation.
 8. A system, comprising: a memory that stores instructions;a processor, coupled to the memory, that facilitates execution of theinstructions to perform operations comprising: receive a first observedtime value associated with radio links between a first pair of radiodevices of a wireless network and a user equipment; access a stored dataset comprising location information correlated to a first referencedifferential time value related to the first pair of radio devices, anda first reference observed time value associated with the first pair ofradio devices; determine a first differential time value based on thefirst observed time value; and determine first location information forthe user equipment based on the stored data set and the firstdifferential time value, wherein the first location informationfacilitates a determination of a location of the user equipment.
 9. Thesystem of claim 8, wherein the first reference observed time value isassociated with a location enabled user equipment in the wirelessnetwork.
 10. The system of claim 9, wherein the location enabled userequipment is a global positioning system enabled user equipment in thewireless network.
 11. The system of claim 8, wherein the first observedtime value is based on a first propagation time for a radio signalbetween a first radio device of the first pair of radio devices and theuser equipment, a second propagation time for a radio signal between asecond radio device of the first pair of radio devices and the userequipment, a first delay time associated with the first radio device ofthe first pair of radio devices, and a second delay time associated withthe second radio device of the first pair of radio devices.
 12. Thesystem of claim 8, further comprising: determine second locationinformation of the stored data set based on a second differential timevalue related to another pair of radio devices of the wireless networkand the user equipment; and determine a probable location for the userequipment based on the first location information and the secondlocation information.
 13. The system of claim 12, wherein the probablelocation is based on a comparison of probable locations comprising thefirst location information to other probable locations comprising thesecond location information to identify shared probable locationsbetween the first location information and the second locationinformation according to a similarity criterion.
 14. The system of claim13, wherein the probable locations comprise bin grid frames and theother probable locations comprise bin grid frames, and wherein a unionfunction of the probable locations and the other probable locationsdetermines the shared probable locations.
 15. A computer-readablestorage medium storing instructions that, in response to execution,cause a system comprising a processor to perform operations, comprising:receiving a first observed time value associated with radio linksbetween a first pair of radio devices of a wireless network and a userequipment; receiving information from a stored data set comprisinglocation information correlated to a first reference differential timevalue related to the first pair of radio devices, and a first referenceobserved time value associated with the first pair of radio devices;determining a first differential time value based on the first observedtime value; and determining first location information for the userequipment based on the information from the stored data set and thefirst differential time value, to facilitate a determination of alocation of the user equipment.
 16. The system of claim 15, wherein thefirst reference observed time value is associated with a locationenabled user equipment in the wireless network.
 17. The system of claim16, wherein the location enabled user equipment is a global positioningsystem enabled user equipment in the wireless network.
 18. The system ofclaim 15, wherein the first observed time value is based on a firstpropagation time for a radio signal between a first radio device of thefirst pair of radio devices and the user equipment, a second propagationtime for a radio signal between a second radio device of the first pairof radio devices and the user equipment, a first delay time associatedwith the first radio device of the first pair of radio devices, and asecond delay time associated with the second radio device of the firstpair of radio devices.
 19. The system of claim 15, further comprising:determining second location information for the user equipment based onthe information from the stored data set based on a second differentialtime value related to another pair of radio devices of the wirelessnetwork and the user equipment; and determining a potential location forthe user equipment based on the first location information and thesecond location information.
 20. The system of claim 19, wherein thepotential location is based on applying a union function to firstpotential locations comprising the first location information withsecond potential locations comprising the second location information toidentify similar potential locations between the first locationinformation and the second location information.